Recent submissions

Comment · 11 years ago
I tested it with Cyanogenmod's stock browser customized with an iPhone user-agent, and it also works fine. It seems like Google is trying to coerce "eligible users" into using the Google Play app, but won't prevent others from accessing the site.
Comment · 12 years ago
Comment · 12 years ago
Comment · 13 years ago
Comment · 13 years ago
For how long do you keep the information?
Comment · 13 years ago
> It's important to understand that when FSF talks about "freedom" they explicitly mean keeping the users free - not the developers. As a developer, I'm personally biased towards being free to other developers, and I think GPL (v2 and esp. v3) are very much pro-user at the cost of being anti-developer.

I don't think that's what the FSF means when they talk about freedom. Freedom is about keeping everyone free--developers and users alike. Copyleft licenses ensure that all derivative works be subject to the same licensing terms as the original work. It's a way to protect the self-perpetuating nature of free software that more permissive licenses fail to achieve.

Comment · 13 years ago
Would you mind expanding upon why you presumably prefer GPLv2 over v3? Additionally, if you prefer the MIT/BSD licensing model, why not choose Apache v2?
Comment · 13 years ago
> FeedSnap's goal is simple: to provide a reliable, capable, and actively maintained FeedBurner replacement for your beloved RSS feed.

In keeping with that goal, is there any chance that you will open source the work you've done under a GPLv3 license? Perhaps the largest problem is that every service claims to be "reliable, capable, and actively maintained" until it isn't.

Comment · 13 years ago
You can estimate the likelihood that a particular sentence is spam by calculating the log sum of n-gram probabilities of sub-sequences in a sentence. These probabilities are obtained from a sufficiently general training set, such as Google's n-gram viewer[1]. You can estimate the probability of a particular sequence of words by summing the log probabilities of each n-gram within that sequence. Using a trigram language model (n = 3), you could estimate the likelihood as follows:

Sentence = "This sentence is semantically and syntactically valid."

P(Sentence) = log(p(START,START,This)) + log(p(START,This,sentence)) + log(p(This,sentence,is)) + log(p(sentence,is,semantically)) + log(p(is,semantically,and)) + log(p(semantically,and,syntactically)) + log(p(and,syntactically,valid)) + log(p(syntactically,valid,.)) + log(p(valid,.,STOP)) + log(p(.,STOP,STOP))

where START and STOP are special symbols that aid in determining the proximity of a word to the beginning and end of a sentence.

If your training set fails to sufficiently generalize, you could use Bayesian inference to estimate the likelihood that the sentence is spam. Under this framework, you'd be calculating the posterior probability of the sentence being spam given the observed sequence of n-grams, which combines (i) the inherent likelihood that any sequence of words is spam and (ii) the compatibility of an observed sequence with (i), which is proportional to the impact it has on (i).

[1] http://storage.googleapis.com/books/ngrams/books/datasetsv2....

Comment · 13 years ago
Comment · 13 years ago
Why not GPLv3?

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